Artificial Societies

MIT Technology Review has an article on Joshua Epstein and artificial life models. (registration required)
Epstein:

“Artificial society modeling allows us to ‘grow’ social structures in silico demonstrating that certain sets of microspecifications are sufficient to generate the macrophenomena of interest.”

In other words, after 250 years we’re back to the Invisible Hand. A small number of factors motivate and inform individual decisions about gathering scarce resources and how to use them. All “complex” social behavior is a result of a large number of simple interactions.
I’ve reviewed Epstein’s Civil Violence Model before.

Epstein: “the trick is to get a lot out, while putting in as little as possible”.

A few functions can describe a whole range of phenomenon without long-winded nuance. Sounds like physics.

So what do we have? Individual making decisions on managing scarce resources with alternative uses. Only a few inputs, only a few motivations, only a few signals, can create a massive range of outputs and diversity of outcomes.

By modeling how individuals interact with each other and their environment, you can create models of civilizations, wars, revolutions, income distribution, and location of cities. There is no need for centralized planning or “social engineering.”

And Agent based models work better than you think.
For instance, slight variations in genetic ability produce wide variations in income. In fact, it produces Pareto Distributions. One of Epstein’s models is Sugarscape. The landscape is a grid where sugar grows and agents roam collecting it.

A few agents with superior vision and low metabolic rates accumulated large sugar stocks. Other agents, with weaker vision and high metabolic rates, subsisted or died in zones where sugar was in short supply. Essentially, Epstein and Axtell found, Sugarscape functioned as a model of a hunter-gatherer society, reproducing a common feature of human societies: skewed wealth distribution. Granted, the notion that crude automata moving around a computer grid suggest that wealth inequality is an innate feature of human existence will be disliked not only by Marxists but by most of the rest of us, given how varied we know our individual experiences to be. Nevertheless, nature is full of peculiarly consistent statistical relationships, which reoccur across dissimilar realms and which statisticians call “power laws.”

Wealth distribution follows simple mathematical rules. The Pareto Distribution will be here so long as men roam the Earth – no “egalitarian” society can ever get rid of it so long as there is diversity in the Complex Adaptive System. Diversity creates a wide range of different inputs – some better than others. The only way to create egalitarianism is to eliminate diversity at the genetic level – which cannot be done.

There are many more models too. Even slight preferential attachment can lead to segregation, as proven by Thomas Schelling.

Agent-base models are growing more complicated to take into account environmental conditions and more in-depth scenarios.

This is very impressive. Epstein and Axtell created an artificial civilization to model the Anasazi.

Epstein and Axtell decided to use their agent-based modeling to create a virtual Anasazi civilization and see how it matched up against the extensive database of settlement patterns and the like assembled by Gumerman and his colleagues. Epstein recalled, “We started over, building the artificial terrain from scratch, with great exactitude.” Elements like climate patterns, maize yields, fluctuations of the water table, and multitudes of other factors went into the model. “The big trick was, Could we come up with good rules for our artificial Anasazi, put them where the real ones were in 900 a.d., and let them run till they grew the true history?” Epstein remembered one session in which his team’s artificial Anasazi established a settlement exactly where Long House, the real Anasazi settlement, had been. “We just sat screaming into the air with gratification. The entire business has come an awfully long way since then. Now there’s many people doing this kind of work.”

I love that. City locations are deterministic based on location of resources and logistical capabilities but we had no idea how they got established. To actually find a process that recreates the location of cities? They’re finally getting somewhere.

Altogether, in fact, Epstein stressed that his models were mostly aimed at achieving explanatory power. “To explain something doesn’t mean that you can predict it,” he said. He pointed out that though we can explain lightning and earthquakes, we can’t forecast either. If we’re hoping, like Asimov, to predict the future, Epstein’s models will disappoint. In fact, because his models give widely divergent results even when their agents are programmed with very simple rules, they indicate that predicting the future will never be possible. Still, Epstein’s artificial societies do more to make plain the hidden mechanisms underlying social shifts–and their unexpected consequences–than any tool that social scientists have hitherto possessed.

Butterfly effect.

Social Science will wind up like Meteorology and Evolutionary Biology – very good at describing the process, but incapable of predicting results because they lack complete information.

The whole concept of centralized planning like the Soviet 5 Year Plans fail because the “central planners” are just individuals who lack information needed to make intelligent decisions.

Now that there is real progress in understanding the mechanism, we can see why individualist models can sufficiently explain all the “complex” phenomenon.

Social Sciences are starting to mature into a real science now. Pseudosciences like sociology and political philosophy are being discarded in favor of quantitative models.

Right now, social sciences are today where physics was in the 1400s. Statistical studies show the end result of interactions, but did not reveal the process behind them. This is akin to the study of astronomy to learn physics. You see planets move, so you have to learn where they are moving before you can guess how.

Eventually, you get somewhere, but you need empirical evidence before you can understand the process. Like how astronomers traced planetary orbits, we discover Gaussian Distributions and Power Laws and so on. One major advancement in social sciences is the shift to studying population statistics. This is similar to the advance in biological evolution.

There have been major advances in uncovering this mechanism. Concepts like genetic algorithms and complex adaptive systems may play a key role.

Agent-based models can simulate economics and biological evolution. Then you find something interesting – the system is deterministic, but it is nonlinear and unpredictable. Even the slightest change in intitial imputs causes radically different results. You also find out that individual agents are the primary decision-maker and everything that seems “complex” comes from many small actions. A very small number of inputs produces a wide range of outputs.

In a manner of speaking, Adam Smith and John Holland and others are the social scientists on par with Copernicus and Galileo. And the English Departments are playing the role of the Catholic Church and Inquisition apparently.

The Social Sciences have not been a real science until very recently. The closest was economics. Unfortunately, the drooling barbarian hordes of the Evil English Departments threatened to conquer the fair kingdom (so to speak).

As Epstein says, “the trick is to get a lot out, while putting in as little as possible”

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2 Responses to “Artificial Societies”

“The whole concept of centralized planning like the Soviet 5 Year Plans fail because the ‘central planners’ are just individuals who lack information needed to make intelligent decisions.”

The point of agent-based modelling is not to show that fully decentralized economies are necessarily superior. Indeed, some ACE (agent-based comp. econ.) modelling takes its inspiration from Keynes, who supported large-scale government involvement in the economy. Agent-based models do not necessarily attain a desirable equilibrium (or non-equilibrium) either. Neither does society. Thomas Schelling talks about that a lot in Micromotives and Macrobehavior. And of course a classic example of his when it comes to suboptimal self-organizing behavior, which you are probably aware of, is neighborhood segregation.

“The Pareto Distribution will be here so long as men roam the Earth – no ‘egalitarian’ society can ever get rid of it so long as there is diversity in the Complex Adaptive System.”

There’s a sort of is/ought question here … I suppose banishing Pareto distributions of wealth altogether is not possible but countries that perform better on metrics like the Human Development Index tend to have somewhat more equal distributions.

“Pseudosciences like sociology and political philosophy are being discarded in favor of quantitative models.”

Well sociology isn’t all bad. Some of what you post is sociology. Social network analysis is sociology. Then there’s sociophysics, akin to econophysics. Etc…

As for describing political philosophy as a “pseudoscience” … well that doesn’t make sense to me. Ethics for example isn’t a science. It’s just ethics. Non-scientific inquiry isn’t necessarily bad. In fact I wouldn’t even call math a science.

“The Social Sciences have not been a real science until very recently.”

Some psychology has been very good since the 19th century, I’d say. Psychophysics for example.

“And the English Departments are playing the role of the Catholic Church and Inquisition apparently.”

You mean those postmodernist bozos? They’re not limited to the English dept. Likewise, some people in the English dept. aren’t pomos. Womyn’s Studies etc. is probably more of a villain.

In any case, I am glad to see some of our future military leaders have a grasp of the very important field of complexity science. Did you hear about the combat simulator EINSTein?